{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPUFFtGZRKhi"
      },
      "source": [
        "# ART Targeted Universal Perturbation\n",
        "Train a PyTorch classifier on MNIST dataset, then attack it with targeted universal adversarial perturbations.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "UqgvEhS7ReTV"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "from torch import optim\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from art.attacks.evasion import TargetedUniversalPerturbation\n",
        "from art.estimators.classification import PyTorchClassifier\n",
        "from art.utils import load_mnist\n",
        "\n",
        "import time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXk56AdlRiKg"
      },
      "source": [
        "Load the MNIST dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "z4K19tJURgKv"
      },
      "outputs": [],
      "source": [
        "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()\n",
        "\n",
        "# Swap axes to PyTorch's NCHW format\n",
        "x_train = np.transpose(x_train, (0, 3, 1, 2)).astype(np.float32)\n",
        "x_test = np.transpose(x_test, (0, 3, 1, 2)).astype(np.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMFPiLy-SPYB"
      },
      "source": [
        "Create the model and train the ART classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "d72RLBmVSo6b"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "    nn.Conv2d(1, 4, 5),\n",
        "    nn.ReLU(),\n",
        "    nn.MaxPool2d(2, 2),\n",
        "    nn.Conv2d(4, 10, 5),\n",
        "    nn.ReLU(),\n",
        "    nn.MaxPool2d(2, 2),\n",
        "    nn.Flatten(),\n",
        "    nn.Linear(4 * 4 * 10, 100),\n",
        "    nn.Linear(100, 10),\n",
        ")\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
        "\n",
        "classifier = PyTorchClassifier(\n",
        "    model=model,\n",
        "    clip_values=(min_pixel_value, max_pixel_value),\n",
        "    loss=criterion,\n",
        "    optimizer=optimizer,\n",
        "    input_shape=(1, 28, 28),\n",
        "    nb_classes=10\n",
        ")\n",
        "\n",
        "classifier.fit(x_train, y_train, batch_size=64, nb_epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ySgYKa6UPlI"
      },
      "source": [
        "Run Targeted Universal Perturbation attack"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gm0L8jpfUToS",
        "outputId": "bd9b6d20-fbd3-4f30-db39-14acc440cfc2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Executed in: 2.46 minutes\n"
          ]
        }
      ],
      "source": [
        "# Create a one-hot encoded target label array, specifying a specific class as the target for the attack\n",
        "TARGET = 0\n",
        "y_target = np.zeros([len(x_train), 10])\n",
        "for i in range(len(x_train)):\n",
        "    y_target[i, TARGET] = 1.0\n",
        "\n",
        "attack = TargetedUniversalPerturbation(\n",
        "    classifier,\n",
        "    max_iter=1,\n",
        "    attacker=\"fgsm\",\n",
        "    attacker_params={\"delta\": 0.6, \"eps\": 0.01, \"targeted\": True, \"verbose\": False},\n",
        ")\n",
        "\n",
        "start_time = time.time()\n",
        "x_train_adv = attack.generate(x_train, y=y_target)\n",
        "print(\"Executed in:\", round((time.time()-start_time)/60, 2), \"minutes\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc2gjvqzUV02"
      },
      "source": [
        "Print attack statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yl880Qs6TVMw",
        "outputId": "2eaa573d-ba18-441c-c894-ff0460d7b47e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attack statistics:\n",
            "Fooling rate: 87.79%\n",
            "Targeted success rate: 97.41%\n",
            "Converged: False\n",
            "\n",
            "Misclassified train samples: 52728\n",
            "Misclassified test samples: 8632\n"
          ]
        }
      ],
      "source": [
        "print(\"Attack statistics:\")\n",
        "print(f\"Fooling rate: {attack.fooling_rate:.2%}\")\n",
        "print(f\"Targeted success rate: {attack.targeted_success_rate:.2%}\")\n",
        "print(f\"Converged: {attack.converged}\")\n",
        "\n",
        "# Evaluate the attack results\n",
        "train_y_pred = np.argmax(classifier.predict(x_train_adv), axis=1)\n",
        "print(\"\\nMisclassified train samples:\", np.sum(np.argmax(y_train, axis=1) != train_y_pred))\n",
        "\n",
        "# Generate adversarial examples for test set\n",
        "x_test_adv = x_test + attack.noise\n",
        "\n",
        "# Evaluate the attack results on the test set\n",
        "test_y_pred = np.argmax(classifier.predict(x_test_adv), axis=1)\n",
        "print(\"Misclassified test samples:\", len(x_test_adv[np.argmax(y_test, axis=1) != test_y_pred]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqLt3NQyTW8Q"
      },
      "source": [
        "Plot some misclassified samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "vjtAGMrzPe7U",
        "outputId": "1cece8ce-1f29-4924-acd6-ee1aebf16583"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
        "\n",
        "for i, ax in enumerate(axes.flat):\n",
        "    ax.imshow(x_test_adv[i, ...].squeeze())\n",
        "    ax.axis(\"off\")\n",
        "    ax.text(\n",
        "        0.5,\n",
        "        -0.05,\n",
        "        f\"True Label: {np.argmax(y_test, axis=1)[i]}, Predicted Label: {test_y_pred[i]}\",\n",
        "        transform=ax.transAxes,\n",
        "        horizontalalignment=\"center\",\n",
        "        verticalalignment=\"center\",\n",
        "    )\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.suptitle(\"Adversarial example and labels\", fontsize=16, y=1.01)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
